5  Appendix B

5.1 Setup

5.1.1 Install Packages

We install the following packages using the groundhog package manager to increase computational reproducibility.

options(repos = c(CRAN = "https://cran.r-project.org")) 

if (!requireNamespace("groundhog", quietly = TRUE)) {
    install.packages("groundhog")
}

pkgs <- c("magrittr", "data.table", "stringr", "Rmisc", "ggplot2",
          "lmtest", "sandwich", "stargazer")

groundhog::groundhog.library(pkg = pkgs,
                             date = "2024-08-01")

rm(pkgs)

5.1.2 Read Data

data      <- readRDS(file="../data/processed/full.Rda")
timeSpent <- data.table::fread(file = "../data/raw/PageTimes-2021-09-15.csv")
raw       <- data.table::fread(file="../data/raw/all_apps_wide_2021-09-15.csv")

5.2 Table B.1

ols_1 <- lm(formula = E1 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E1 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E1 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E1 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E1 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E1 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E1",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "part2", "contradiction x stage 2", "interval x stage2", "both x stage2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE,
            style = "qje")

Linear regressions: Treatment effects on E1

Linear regressions: Treatment effects on E1
E1
full
confirmation
contradiction
point
interval
both
(1)
(2)
(3)
(4)
(5)
(6)
contradiction
-0.962
-1.492
0.951
-2.435
(1.080)
(1.760)
(1.993)
(1.855)
both
1.331
3.773**
(1.836)
(1.923)
interval
2.430
1.487
(1.759)
(1.856)
part2
-1.036
0.955
7.468***
0.955
-1.634
-2.504*
(0.846)
(1.500)
(1.791)
(1.500)
(1.498)
(1.391)
contradiction x stage 2
7.563***
6.513***
4.269*
12.098***
(1.315)
(2.336)
(2.232)
(2.251)
interval x stage2
-2.589
-4.833**
(2.120)
(2.437)
both x stage2
-3.459*
2.126
(2.046)
(2.517)
Constant
47.893***
46.653***
45.161***
46.653***
47.984***
49.083***
(0.747)
(1.193)
(1.294)
(1.193)
(1.395)
(1.293)
N
3,010
1,490
1,520
1,014
1,002
994
R2
0.014
0.002
0.024
0.015
0.006
0.028
Adjusted R2
0.013
-0.002
0.021
0.012
0.003
0.025
Residual Std. Error
23.210
22.727
23.652
22.494
23.624
23.473
F Statistic
13.967***
0.530
7.531***
5.226***
2.152*
9.393***
Notes:
***Significant at the 1 percent level.
**Significant at the 5 percent level.
*Significant at the 10 percent level.

5.3 Table B.2

ols_1 <- lm(formula = E2 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E2 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E2 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E2 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E2 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E2 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E2",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "part2", "contradiction x stage 2", "interval x stage2", "both x stage2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE,
            style = "qje")

Linear regressions: Treatment effects on E2

Linear regressions: Treatment effects on E2
E2
full
confirmation
contradiction
point
interval
both
(1)
(2)
(3)
(4)
(5)
(6)
contradiction
-1.235
-1.497
-1.578
-0.612
(1.072)
(1.779)
(1.943)
(1.852)
both
0.174
0.092
(1.867)
(1.858)
interval
1.558
2.443
(1.806)
(1.825)
part2
3.535***
4.227***
-3.103**
4.227***
2.998**
3.348**
(0.782)
(1.159)
(1.407)
(1.159)
(1.468)
(1.433)
contradiction x stage 2
-5.636***
-7.331***
-3.812*
-5.768***
(1.125)
(1.824)
(1.977)
(2.059)
interval x stage2
-1.230
2.289
(1.871)
(1.932)
both x stage2
-0.879
0.683
(1.843)
(2.041)
Constant
50.681***
50.108***
48.611***
50.108***
50.282***
51.666***
(0.757)
(1.250)
(1.265)
(1.250)
(1.387)
(1.304)
N
3,010
1,490
1,520
1,014
1,002
994
R2
0.013
0.007
0.005
0.023
0.008
0.010
Adjusted R2
0.012
0.004
0.002
0.020
0.005
0.007
Residual Std. Error
21.997
22.066
21.941
20.890
22.813
22.276
F Statistic
12.879***
2.214*
1.655
7.827***
2.700**
3.462**
Notes:
***Significant at the 1 percent level.
**Significant at the 5 percent level.
*Significant at the 10 percent level.

5.4 Table B.3

ols_1 <- lm(formula = E3 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E3 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E3 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E3 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E3 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E3 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E3",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "part2", "contradiction x stage 2", "interval x stage2", "both x stage2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE,
            style = "qje")

Linear regressions: Treatment effects on E3

Linear regressions: Treatment effects on E3
E3
full
confirmation
contradiction
point
interval
both
(1)
(2)
(3)
(4)
(5)
(6)
contradiction
-0.317
1.231
0.356
-2.623
(1.049)
(1.814)
(1.864)
(1.767)
both
2.199
1.323
(1.787)
(1.889)
interval
4.813***
0.958
(1.752)
(1.828)
part2
0.148
2.161
-5.425***
2.161
-0.006
-1.779
(0.818)
(1.477)
(1.635)
(1.477)
(1.283)
(1.469)
contradiction x stage 2
-3.721***
-7.585***
-2.558
-0.971
(1.217)
(2.204)
(1.972)
(2.136)
interval x stage2
-2.167
2.861
(1.956)
(2.217)
both x stage2
-3.940*
2.675
(2.083)
(2.253)
Constant
48.591***
46.278***
47.510***
46.278***
48.477***
51.091***
(0.733)
(1.204)
(1.357)
(1.204)
(1.321)
(1.274)
N
3,010
1,490
1,520
1,014
1,002
994
R2
0.006
0.004
0.009
0.012
0.002
0.007
Adjusted R2
0.005
0.001
0.006
0.009
-0.001
0.004
Residual Std. Error
22.475
21.461
23.399
22.160
22.847
22.373
F Statistic
5.563***
1.320
2.815**
4.050***
0.678
2.489*
Notes:
***Significant at the 1 percent level.
**Significant at the 5 percent level.
*Significant at the 10 percent level.

5.5 Table B.4

ols_1 <- lm(formula = E12 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E12 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E12 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E12 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E12 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E12 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E12",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "part2", "contradiction x stage 2", "interval x stage2", "both x stage2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE,
            style = "qje")

Linear regressions: Treatment effects on E12

Linear regressions: Treatment effects on E12
E12
full
confirmation
contradiction
point
interval
both
(1)
(2)
(3)
(4)
(5)
(6)
contradiction
-0.453
0.446
-1.087
-0.776
(1.031)
(1.772)
(1.754)
(1.838)
both
2.072
0.539
(1.743)
(1.783)
interval
1.672
0.451
(1.760)
(1.850)
part2
1.817**
3.090**
4.460***
3.090**
-0.401
2.684*
(0.828)
(1.365)
(1.611)
(1.365)
(1.499)
(1.440)
contradiction x stage 2
3.950***
1.370
5.012**
5.592***
(1.198)
(2.112)
(2.050)
(2.065)
interval x stage2
-3.491*
0.150
(2.027)
(2.134)
both x stage2
-0.406
3.816*
(1.984)
(2.187)
Constant
58.358***
57.127***
57.573***
57.127***
59.200***
58.800***
(0.716)
(1.230)
(1.276)
(1.230)
(1.234)
(1.258)
N
3,010
1,490
1,520
1,014
1,002
994
R2
0.010
0.004
0.019
0.008
0.007
0.021
Adjusted R2
0.009
0.001
0.015
0.005
0.004
0.018
Residual Std. Error
22.155
21.169
23.078
22.444
21.919
22.090
F Statistic
10.599***
1.214
5.751***
2.679**
2.266*
7.149***
Notes:
***Significant at the 1 percent level.
**Significant at the 5 percent level.
*Significant at the 10 percent level.

5.6 Table B.5

ols_1 <- lm(formula = E13 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E13 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E13 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E13 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E13 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E13 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E13",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "part2", "contradiction x stage 2", "interval x stage2", "both x stage2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE,
            style = "qje")

Linear regressions: Treatment effects on E13

Linear regressions: Treatment effects on E13
E13
full
confirmation
contradiction
point
interval
both
(1)
(2)
(3)
(4)
(5)
(6)
contradiction
0.922
-2.686
4.258**
1.173
(1.037)
(1.743)
(1.846)
(1.792)
both
-1.358
5.586***
(1.811)
(1.779)
interval
0.479
4.338**
(1.749)
(1.786)
part2
-1.549*
-1.333
4.212***
-1.333
0.068
-3.362**
(0.873)
(1.414)
(1.579)
(1.414)
(1.581)
(1.543)
contradiction x stage 2
3.721***
5.546***
-0.192
5.848***
(1.221)
(2.120)
(2.054)
(2.176)
interval x stage2
1.401
-4.336**
(2.121)
(2.052)
both x stage2
-2.029
-1.726
(2.093)
(2.201)
Constant
55.130***
55.414***
52.728***
55.414***
54.056***
55.893***
(0.736)
(1.200)
(1.264)
(1.200)
(1.357)
(1.273)
N
3,010
1,490
1,520
1,014
1,002
994
R2
0.006
0.002
0.010
0.005
0.009
0.013
Adjusted R2
0.005
-0.001
0.007
0.002
0.006
0.010
Residual Std. Error
21.788
22.070
21.473
21.291
21.762
22.259
F Statistic
5.977***
0.717
3.041***
1.812
3.054**
4.266***
Notes:
***Significant at the 1 percent level.
**Significant at the 5 percent level.
*Significant at the 10 percent level.

5.7 Table B.6

ols_1 <- lm(formula = E23 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E23 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E23 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E23 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E23 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E23 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E23",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "part2", "contradiction x stage 2", "interval x stage2", "both x stage2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE,
            style = "qje")

Linear regressions: Treatment effects on E23

Linear regressions: Treatment effects on E23
E23
full
confirmation
contradiction
point
interval
both
(1)
(2)
(3)
(4)
(5)
(6)
contradiction
1.513
0.254
1.193
3.086
(1.154)
(2.075)
(1.963)
(1.959)
both
0.722
1.661
(2.047)
(1.992)
interval
0.306
3.139
(2.028)
(2.006)
part2
3.099***
3.308**
-7.562***
3.308**
2.074
3.893**
(0.855)
(1.389)
(1.812)
(1.389)
(1.458)
(1.597)
contradiction x stage 2
-10.200***
-10.869***
-7.545***
-12.211***
(1.326)
(2.283)
(2.198)
(2.417)
interval x stage2
-1.234
2.091
(2.013)
(2.447)
both x stage2
0.585
-0.756
(2.116)
(2.564)
Constant
59.658***
59.322***
59.575***
59.322***
60.043***
59.628***
(0.829)
(1.452)
(1.481)
(1.453)
(1.442)
(1.416)
N
3,010
1,490
1,520
1,014
1,002
994
R2
0.020
0.005
0.027
0.026
0.012
0.024
Adjusted R2
0.019
0.001
0.023
0.023
0.009
0.021
Residual Std. Error
23.195
23.236
23.167
23.966
22.547
23.058
F Statistic
20.085***
1.441
8.284***
8.939***
3.966***
8.016***
Notes:
***Significant at the 1 percent level.
**Significant at the 5 percent level.
*Significant at the 10 percent level.